Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stages from source code is key for better understanding ML practices.
ProofPaper ↗
Key points
- However, the diversity caused by the constant evolution of ML (e.g., algorithms, datasets) makes this task challenging.
- Objective: We evaluate whether Small Language Models (SLMs) can leverage their code understanding and classification abilities to address these limitations, and enhance our understanding of practices in ML.
- An additional Cochran's Q test examines how taxonomy definition variations affect the SLM performance.
- Conclusions: Limitations of existing classification methods bias our understanding of ML practices.
Sources (1)
- [1]Using Small Language Models to Reverse-Engineer Machine Learning Pipelines StructuresarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 03:35 PM
Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stages from source code is key for better understanding ML practices.
However, the diversity caused by the constant evolution of ML (e.g., algorithms, datasets) makes this task challenging.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 7, 2026Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning
- Oct 7, 2026Q-Learning with Scalar Adjoint Matching
- Oct 6, 2026Unlocking Earth AI’s planetary geospatial foundation models for global public health
- Oct 6, 2026Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
- Sep 30, 2026Expanding AI Storage Access with NVIDIA cuObject and the NVIDIA SCADA Server SDK
- Sep 28, 2026openai/openai-python v3.20.0